Unsupervised image representation learning [1]}, [2]}, [3]}, [4]}, [5]} has attracted great attention recently, which attempts to learn useful knowledge from massive unlabeled data and transfer to various downstream tasks.
Nevertheless, the efforts in video counterpart are still inapparent, though the multi-modal nature is particularly suitable for unsupervised learning by providing intensive coherence and variation information.
This paper focuses on the Video-Audio Unsupervised Learning task [6]}, [7]}, [8]}, [6]}, which aims at exploring cross-modal clues to simultaneously learn visual and audio representations from massive unlabeled videos.
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